485,832 research outputs found

    Applying MDL to Learning Best Model Granularity

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    The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

    Taking the bite out of automated naming of characters in TV video

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    We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”

    Natural Kinds in Evolution and Systematics: Metaphysical and Epistemological Considerations

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    Despite the traditional focus on metaphysical issues in discussions of natural kinds in biology, epistemological considerations are at least as important. By revisiting the debate as to whether taxa are kinds or individuals, I argue that both accounts are metaphysically compatible but one or the other approach can be pragmatically preferable depending on the epistemic context. Recent objections against construing species as homeostatic property cluster kinds are also addressed. The second part of the paper broadens the perspective by considering homologues as another example of natural kinds, comparing them with analogues as functionally defined kinds. Given that there are various types of natural kinds, I discuss the different theoretical purposes served by diverse kind concepts, suggesting that there is no clear-cut distinction between natural kinds and other kinds, such as functional kinds. Rather than attempting to offer a unique metaphysical account of ‘natural’ kind, a more fruitful approach consists in the epistemological study of how different natural kind concepts are employed in scientific reasoning

    A study of systems implementation languages for the POCCNET system

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    The results are presented of a study of systems implementation languages for the Payload Operations Control Center Network (POCCNET). Criteria are developed for evaluating the languages, and fifteen existing languages are evaluated on the basis of these criteria

    Maclisp extensions

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    A common subset of selected facilities available in Maclisp and its derivatives (PDP-10 and Multics Maclisp, Lisp Machine Lisp (Zetalisp), and NIL) is decribed. The object is to add in writing code which can run compatibly in more than one of these environments

    An Introduction to Programming for Bioscientists: A Python-based Primer

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    Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

    The Contextual Character of Modal Interpretations of Quantum Mechanics

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    In this article we discuss the contextual character of quantum mechanics in the framework of modal interpretations. We investigate its historical origin and relate contemporary modal interpretations to those proposed by M. Born and W. Heisenberg. We present then a general characterization of what we consider to be a modal interpretation. Following previous papers in which we have introduced modalities in the Kochen-Specker theorem, we investigate the consequences of these theorems in relation to the modal interpretations of quantum mechanics.Comment: 21 pages, no figures, preprint submitted to SHPM
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